M01: Lecture Notes

Introduction to Generative AI & Business Applications

M01:
Notes
Lecture
Lecture covering Introduction to Generative AI & Business Applications.
Published

November 21, 2024

Modified

November 19, 2025

1 Your Instructor

1.1 Nakul R. Padalkar


1.2 AD698 – Learning Path


2 What This Course Is About

This course explores how modern generative AI systems reshape analytical workflows and business decision-making. You will learn:

  • How natural language functions as a data type
  • How LLMs process text, structure information, and generate content
  • How GenAI integrates with business analytics pipelines
  • How to build practical generative-AI solutions with Python, APIs, and automation
  • How to critically evaluate model outputs, reliability, and risks
  • How to design human–AI workflows and deploy GenAI tools effectively in organizations

3 Why This Course Matters for Your Career

  • Every analytics job now intersects with LLMs.
    • Analysts who master prompt engineering, RAG, automation, and LLM-based workflows will dramatically outperform peers.
  • AI-augmented analysts produce more work, more insight, and higher-value deliverables.
  • Organizations expect you to understand:
    • How AI reads and structures unstructured data
    • How to integrate AI into dashboards, apps, and analytical workflows
    • Responsible use, privacy, transparency, and governance principles
  • This course trains you in supporting interviews, project pitches, and job applications while responding to the AI Augmented Analyst roles.

4 Course Overview


5 Course Structure

5.0.1 Modules 0–7

Each module has two lectures, one lab, and one assignment. Topics include:

  1. Introduction to LLMs and Natural Language
  2. Prompt Engineering & In-Context Learning
  3. Vector Search & RAG Systems
  4. AI-Driven Data Analytics
  5. Automation, Agents & Workflows
  6. Generative AI APIs for Business
  7. Evaluation, Safety, and Governance
  8. Deployment, Documentation & Portfolio

6 Course Grading*

Class Activity Count Points Max Points
AWS Academy Generative AI Foundations 1 100 100
Github+githubpages ePortfolio Creation 1 40 40
Labs* 10 20 200
Assignment 5 50 250
Managerial report with Application Demo 1 80 80
Git and git website setup 1 - -
Api and data gathering 1 - -
Data cleaning and EDA 1 - -
Analytics, including full website 1 - -
Group Project Presentation 1 40 40
Group Feedback 1 40 40
Total - - 750

7 Participation Components

7.1 LLM Lab Practice

  • Weekly hands-on exercises using LLMs
  • Focus on prompting, automation, and applied business tasks

7.2 Tooling: GitHub, Python, APIs

  • You will maintain a public portfolio repository
  • Every assignment and project will be submitted via GitHub

8 In-Class Labs

Labs are short, guided activities where you:

  • Work directly with LLM models
  • Explore generative workflows
  • Build first-draft AI tools that feed into assignments
  • Analyze data, automate tasks, and generate outputs
  • Write practical reports and visualizations

On-campus: must submit weekly Online: optional but recommended for practice


9 Individual Assignments

Four structured assignments that build core competencies:

  1. Prompt Engineering & Automated Analytics
  2. RAG and Vector Search
  3. AI Pipeline for Business Intelligence
  4. Automated Workflow + API Integration

10 Group Project

A semester-long generative-AI solution to a business problem.

10.0.1 Components

Component Points Description
Milestones via GitHub 80 Repo setup, design docs, data prep, RAG prototype, LLM workflow, final output
Presentation 40 Demonstrates model workflow and business impact
Peer Evaluation 40 Collaboration, clarity, and contributions

Total: 160 points


11 Course Site

  • Central hub for all materials
  • Contains lecture notes, slides, labs, and links
  • Includes schedule, deadlines, and announcements
  • Updated continuously as the course progresses


12 Office Hours & Consultations

  • Listed on Blackboard
  • Weekly sessions for troubleshooting code, labs, and assignments
  • Dedicated project support sessions (Saturday mornings on Zoom)

13 Next: Lecture 1 — Natural Language & the Generative AI Landscape